DAS-YOLOv13: Dual-Axis Attention and Feature Fusion Model for Wafer Surface Defect Detection

Wafer defects in semiconductor manufacturing can directly damage the physical structure and circuit integrity of wafers, leading to the functional failure of chips. To address this problem, this paper proposes a Dual-Axis Attention-enhanced multi-scale fusion You Only Look Once version 13 (DAS-YOLOv13) model. Based on YOLOv13n, the model is specifically designed for the fast and accurate detection of tiny, multi-scale defects on wafer surfaces. It integrates innovative components such as a dual-axis attention module, an adaptive dynamic multi-scale representation module, and a self-modulation feature aggregation module. By enhancing salient feature expression, improving cross-scale representation capability, and optimizing deep semantic fusion strategies, the model achieves effective defect detection. On the wafer defect dataset, the DAS-YOLOv13 model achieves a mean Average Precision (mAP) of 74.2%, which is 4.3% higher than that of YOLOv13n; the Average Precision at an Intersection over Union (IoU) threshold of 50% (mAP50) reaches 92.9%. The results demonstrate that DAS-YOLOv13 effectively improves the detection accuracy of tiny, multi-scale defects through structural optimization. It provides a reliable solution for high-precision wafer detection in semiconductor manufacturing and can be seamlessly integrated into high-precision semiconductor automated inspection scenarios.